A single hyperparameter least-squares model with Tikhonov regularization [1]}, also known as ridge regression [2]}. The lower level loss takes the form \(f_L(z, p) = {\left|\left|X z - Y\right|\right|}_\text{F}^2 + 10^p {\left|\left|z\right|\right|}_\text{F}^2\) . The features are raw image pixels and a bias term.
